Amplitude vs Heap (2026): Which Product Analytics Tool Wins?
TL;DR: Choose Amplitude if you want the deepest behavioral analysis, built-in experimentation, and a mature enterprise platform — priced on Monthly Tracked Users (MTUs). Choose Heap (now part of Contentsquare) if you want autocapture: every click and page view recorded retroactively, so you can answer questions you didn't think to instrument, with data in minutes instead of weeks. But both tools share one blind spot: product analytics tells you what users did and where they dropped off — never why. To close that gap, Koji runs AI-moderated voice interviews at scale and themes hundreds of "why" conversations into a report in hours. Koji starts free, then €29/month.
Amplitude vs Heap at a glance
| Amplitude | Heap (by Contentsquare) | |
|---|---|---|
| Best for | Deep behavioral analysis, experimentation | Fast time-to-value, retroactive analysis |
| Data capture | Manual event instrumentation | Autocapture (retroactive, no-code) |
| Pricing model | Monthly Tracked Users (MTU) | Session-based, custom quotes |
| Free plan | Starter: ~10,000 MTUs / ~2M events | Free: up to ~10,000 sessions/month |
| Paid entry | Plus (~$49/month) | Growth/Pro/Premier (custom, ~$3,600+/yr) |
| Standout feature | Cohorts, A/B testing, predictions | Autocapture + session replay |
| Shared blind spot | Tells you what, not why | Tells you what, not why |
Heap: autocapture and time-to-value
Heap's whole pitch is that you shouldn't have to decide in advance which events matter. Instead of manually instrumenting each button and funnel step, Heap autocaptures every interaction on your site or app, then lets you define events retroactively — even for behavior that happened before you thought to track it. Teams consistently rate this as the standout: autocapture eliminates the event-taxonomy planning phase that typically delays a proper analytics rollout by weeks, and reviewers report getting usable data in minutes rather than the days or weeks it takes to instrument a manual tool correctly.
After Heap was acquired by Contentsquare in 2023, it gained session replay and UX/experience-analytics features, positioning it as a bridge between raw product analytics and digital-experience monitoring. The free plan covers up to roughly 10,000 sessions/month, which is generous for early validation.
Where Heap falls short: startups often find the jump from free to paid abrupt, and advanced capabilities like data export and Connect are flagged as costly add-ons. Its statistical depth for experimentation and predictive cohorts doesn't match Amplitude's.
Amplitude: behavioral depth and experimentation
Amplitude is the mature, analysis-heavy end of the market. It's built for teams that live in cohorts, retention curves, behavioral segmentation, and built-in A/B testing and predictions. If you want to ask "which behaviors in week one predict retention at day 90?" and then run an experiment against that hypothesis, Amplitude is the stronger platform.
The trade-off is MTU pricing. The free Starter plan is capped at 10,000 monthly tracked users and 2 million events per month. Beyond that, cost scales with your user count — and it climbs fast. Based on aggregated deal data, contracts covering 100,000 to 250,000 MTUs typically range from $40,000 to $80,000 per year. MTU billing is also less predictable than event- or session-based models: a single marketing campaign that inflates your user count can spike the bill.
Where Amplitude falls short: manual instrumentation means you can only analyze what you thought to track, so late-arriving questions require new instrumentation and a wait for data to accumulate — the exact problem Heap's autocapture solves.
Head-to-head: the decision that actually matters
- Speed to first insight: Heap wins. Autocapture means data in minutes; Amplitude's manual taxonomy can take weeks to get right.
- Analytical depth and experimentation: Amplitude wins. Cohorts, predictions, and native A/B testing are more sophisticated.
- Pricing predictability: roughly a wash — both use consumption-style models that get expensive at scale, and neither publishes transparent enterprise pricing.
- Experience analytics: Heap edges ahead post-Contentsquare with session replay bundled in.
But notice what neither bullet resolves: why users behaved the way the charts show. That question doesn't live in the clickstream.
The blind spot both share: the "why"
Amplitude and Heap are both behavioral tools. They observe what users did and where they dropped off with precision — but they never talk to a customer. When a funnel collapses at step three, analytics tells you that it happened and how many users it happened to. The reason lives in the customer's head: the pricing felt risky, the value wasn't clear, a competitor was already in the workflow, the copy created the wrong expectation.
You can stare at a retention curve forever and never recover that sentence. This is the structural limit of every product-analytics tool, no matter how good its autocapture or its cohorts.
Koji: the AI-native "why" layer
Koji is an AI-native customer research platform built to answer exactly the question analytics can't. Instead of watching behavior, Koji runs the conversation — an AI moderator conducts voice or text interviews asynchronously, at the scale of a survey, and decides in real time which follow-up questions to ask. It probes like a skilled researcher ("You mentioned the pricing felt risky — what would have made it feel safe?"), with no moderator bias and no scheduling.
What makes it a genuine research tool rather than a chatbot:
- AI-moderated voice interviews with adaptive follow-ups — see how AI voice interviews work.
- Six structured question types — open_ended, scale, single_choice, multiple_choice, ranking, and yes_no — so a single study captures both the quantitative numbers your analytics already shows and the qualitative why it cannot.
- Automatic thematic analysis that themes hundreds of conversations into patterns — the thematic analysis that would otherwise take an analyst days.
- One-click reports you can share in hours, not weeks.
The proven workflow: analytics + Koji
The highest-leverage teams don't choose between behavior and reasoning — they chain them:
- Let Amplitude or Heap flag where users drop off — the funnel step, the churned cohort, the feature nobody adopts.
- Let Koji interview the users in that cohort to learn why — using targeted customer interview questions delivered by an AI moderator.
- Ship the fix, then watch the same funnel in analytics to confirm it moved.
Analytics tells you where to dig; Koji tells you what you'll find. One shows the what; the other recovers the why — in the customer's own words.
A worked example: the checkout drop-off
Say your funnel report shows a 38% drop between "added payment method" and "completed purchase." Amplitude can slice that cohort by plan, device, and acquisition channel; Heap can retroactively define a "hesitated at payment" event you never instrumented and show you it correlates with mobile Safari users. That is genuinely useful — you now know precisely who is dropping and where.
But you still don't know why. Was it an unexpected fee? A trust gap at the card form? A comparison tab open to a competitor? A Koji study fielded to that exact cohort — "Walk me through the last time you nearly bought but didn't" — returns the sentence that fixes it, often within a day. The analytics narrowed the population from millions to a few thousand; Koji turned those few thousand into a reason you can act on. That is the difference between knowing your conversion rate and knowing your conversion problem.
Bottom line
Amplitude vs Heap is a real decision: Amplitude for behavioral depth and experimentation, Heap for autocapture and speed to insight. But whichever you pick, you've still only measured behavior. The reason behind every drop-off, churn, and non-adoption is a conversation you haven't had yet — and that's the layer Koji owns.
Ready to hear the why behind your numbers? Start free with Koji — 10 interview credits, no credit card, then €29/month. From question to insight in hours, not weeks.
